6 citations · 7 across the 4 of their papers we have counts for
4 papers
Energy-Dissipative Evolutionary Deep Operator Neural Networks
Jiahao Zhang, Shiheng Zhang, Jie Shen +1
Energy-Dissipative Evolutionary Deep Operator Neural Network is an operator learning neural network. It is designed to seed numerical solutions for a class of partial differential…
RMFGP: Rotated Multi-fidelity Gaussian process with Dimension Reduction for High-dimensional Uncertainty Quantification
Jiahao Zhang, Shiqi Zhang, Guang Lin
Multi-fidelity modelling arises in many situations in computational science and engineering world. It enables accurate inference even when only a small set of accurate data is avai…
MultiAuto-DeepONet: A Multi-resolution Autoencoder DeepONet for Nonlinear Dimension Reduction, Uncertainty Quantification and Operator Learning of Forward and Inverse Stochastic Problems
Jiahao Zhang, Shiqi Zhang, Guang Lin
A new data-driven method for operator learning of stochastic differential equations(SDE) is proposed in this paper. The central goal is to solve forward and inverse stochastic prob…
PAGP: A physics-assisted Gaussian process framework with active learning for forward and inverse problems of partial differential equations
Jiahao Zhang, Shiqi Zhang, Guang Lin
In this work, a Gaussian process regression(GPR) model incorporated with given physical information in partial differential equations(PDEs) is developed: physics-assisted Gaussian…